code: def on_algorithm_init(self, *, algorithm, **kwargs):
      policy = Policy.from_checkpoint(
          self._checkpoint_dir, policy_ids=[OPPONENT_POLICY_ID]
      )
  
      # Add restored policy to Algorithm.
      # Note that this policy doesn't have to be trained with the same algorithm
      # of the training stack. You can even mix up TF policies with a Torch stack.
      algorithm.add_policy(
          policy_id=OPPONENT_POLICY_ID,
          policy=policy,
          evaluation_workers=True,
      )
  
file_path: ray/python/ray/rllib/examples/connectors/self_play_with_policy_checkpoint.py
imports: import argparse
  from functools import partial
  import os
  import tempfile
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.algorithms.sac import SACConfig
  from ray.rllib.env.utils import try_import_pyspiel
  from ray.rllib.env.wrappers.open_spiel import OpenSpielEnv
  from ray.rllib.examples.connectors.prepare_checkpoint import create_open_spiel_checkpoint
  from ray.rllib.policy.policy import Policy
  from ray.tune import CLIReporter, register_env
label_desc: Use image node_id 2 for calling the AddPolicyCallback obj's underlying member method code with example usage: obj.on_algorithm_init() without return types